Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations1200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory791.0 KiB
Average record size in memory675.0 B

Variable types

Numeric11
Categorical14
Boolean2

Alerts

Age is highly overall correlated with TotalWorkExperienceInYearsHigh correlation
EmpDepartment is highly overall correlated with EmpJobRoleHigh correlation
EmpJobLevel is highly overall correlated with TotalWorkExperienceInYearsHigh correlation
EmpJobRole is highly overall correlated with EmpDepartmentHigh correlation
ExperienceYearsAtThisCompany is highly overall correlated with ExperienceYearsInCurrentRole and 3 other fieldsHigh correlation
ExperienceYearsInCurrentRole is highly overall correlated with ExperienceYearsAtThisCompany and 2 other fieldsHigh correlation
TotalWorkExperienceInYears is highly overall correlated with Age and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with ExperienceYearsAtThisCompany and 1 other fieldsHigh correlation
NumCompaniesWorked has 156 (13.0%) zeros Zeros
TrainingTimesLastYear has 44 (3.7%) zeros Zeros
ExperienceYearsAtThisCompany has 36 (3.0%) zeros Zeros
ExperienceYearsInCurrentRole has 190 (15.8%) zeros Zeros
YearsSinceLastPromotion has 469 (39.1%) zeros Zeros
YearsWithCurrManager has 215 (17.9%) zeros Zeros

Reproduction

Analysis started2025-03-19 12:58:56.726490
Analysis finished2025-03-19 12:59:26.450690
Duration29.72 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.918333
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:26.770549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.0872885
Coefficient of variation (CV)0.24614569
Kurtosis-0.43099958
Mean36.918333
Median Absolute Deviation (MAD)6
Skewness0.38414496
Sum44302
Variance82.578813
MonotonicityNot monotonic
2025-03-19T18:29:27.077349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 71
 
5.9%
35 64
 
5.3%
36 60
 
5.0%
31 57
 
4.8%
29 51
 
4.2%
38 48
 
4.0%
32 46
 
3.8%
40 46
 
3.8%
33 46
 
3.8%
27 43
 
3.6%
Other values (33) 668
55.7%
ValueCountFrequency (%)
18 8
 
0.7%
19 8
 
0.7%
20 6
 
0.5%
21 11
 
0.9%
22 15
 
1.2%
23 9
 
0.8%
24 20
1.7%
25 24
2.0%
26 33
2.8%
27 43
3.6%
ValueCountFrequency (%)
60 3
 
0.2%
59 6
 
0.5%
58 11
0.9%
57 4
 
0.3%
56 11
0.9%
55 17
1.4%
54 16
1.3%
53 15
1.2%
52 15
1.2%
51 14
1.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size72.5 KiB
Male
725 
Female
475 

Length

Max length6
Median length4
Mean length4.7916667
Min length4

Characters and Unicode

Total characters5750
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 725
60.4%
Female 475
39.6%

Length

2025-03-19T18:29:27.443372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:27.684738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 725
60.4%
female 475
39.6%

Most occurring characters

ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1675
29.1%
a 1200
20.9%
l 1200
20.9%
M 725
12.6%
F 475
 
8.3%
m 475
 
8.3%
Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Life Sciences
492 
Medical
384 
Marketing
137 
Technical Degree
100 
Other
66 

Length

Max length16
Median length15
Mean length10.468333
Min length5

Characters and Unicode

Total characters12562
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarketing
2nd rowMarketing
3rd rowLife Sciences
4th rowHuman Resources
5th rowMarketing

Common Values

ValueCountFrequency (%)
Life Sciences 492
41.0%
Medical 384
32.0%
Marketing 137
 
11.4%
Technical Degree 100
 
8.3%
Other 66
 
5.5%
Human Resources 21
 
1.8%

Length

2025-03-19T18:29:27.907501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:28.101821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
life 492
27.1%
sciences 492
27.1%
medical 384
21.2%
marketing 137
 
7.6%
technical 100
 
5.5%
degree 100
 
5.5%
other 66
 
3.6%
human 21
 
1.2%
resources 21
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 2505
19.9%
i 1605
12.8%
c 1589
12.6%
n 750
 
6.0%
a 642
 
5.1%
613
 
4.9%
s 534
 
4.3%
M 521
 
4.1%
L 492
 
3.9%
f 492
 
3.9%
Other values (16) 2819
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2505
19.9%
i 1605
12.8%
c 1589
12.6%
n 750
 
6.0%
a 642
 
5.1%
613
 
4.9%
s 534
 
4.3%
M 521
 
4.1%
L 492
 
3.9%
f 492
 
3.9%
Other values (16) 2819
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2505
19.9%
i 1605
12.8%
c 1589
12.6%
n 750
 
6.0%
a 642
 
5.1%
613
 
4.9%
s 534
 
4.3%
M 521
 
4.1%
L 492
 
3.9%
f 492
 
3.9%
Other values (16) 2819
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2505
19.9%
i 1605
12.8%
c 1589
12.6%
n 750
 
6.0%
a 642
 
5.1%
613
 
4.9%
s 534
 
4.3%
M 521
 
4.1%
L 492
 
3.9%
f 492
 
3.9%
Other values (16) 2819
22.4%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
Married
548 
Single
384 
Divorced
268 

Length

Max length8
Median length7
Mean length6.9033333
Min length6

Characters and Unicode

Total characters8284
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowDivorced
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 548
45.7%
Single 384
32.0%
Divorced 268
22.3%

Length

2025-03-19T18:29:28.539265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:28.783761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 548
45.7%
single 384
32.0%
divorced 268
22.3%

Most occurring characters

ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1364
16.5%
i 1200
14.5%
e 1200
14.5%
d 816
9.9%
M 548
6.6%
a 548
6.6%
S 384
 
4.6%
n 384
 
4.6%
g 384
 
4.6%
l 384
 
4.6%
Other values (4) 1072
12.9%

EmpDepartment
Categorical

High correlation 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size81.4 KiB
Sales
373 
Development
361 
Research & Development
343 
Human Resources
54 
Finance
49 

Length

Max length22
Median length15
Mean length12.3125
Min length5

Characters and Unicode

Total characters14775
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowSales
3rd rowSales
4th rowHuman Resources
5th rowSales

Common Values

ValueCountFrequency (%)
Sales 373
31.1%
Development 361
30.1%
Research & Development 343
28.6%
Human Resources 54
 
4.5%
Finance 49
 
4.1%
Data Science 20
 
1.7%

Length

2025-03-19T18:29:29.111537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:29.324340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
development 704
35.9%
sales 373
19.0%
research 343
17.5%
343
17.5%
human 54
 
2.8%
resources 54
 
2.8%
finance 49
 
2.5%
data 20
 
1.0%
science 20
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 3368
22.8%
l 1077
 
7.3%
n 876
 
5.9%
a 859
 
5.8%
s 824
 
5.6%
760
 
5.1%
o 758
 
5.1%
m 758
 
5.1%
t 724
 
4.9%
D 724
 
4.9%
Other values (12) 4047
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3368
22.8%
l 1077
 
7.3%
n 876
 
5.9%
a 859
 
5.8%
s 824
 
5.6%
760
 
5.1%
o 758
 
5.1%
m 758
 
5.1%
t 724
 
4.9%
D 724
 
4.9%
Other values (12) 4047
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3368
22.8%
l 1077
 
7.3%
n 876
 
5.9%
a 859
 
5.8%
s 824
 
5.6%
760
 
5.1%
o 758
 
5.1%
m 758
 
5.1%
t 724
 
4.9%
D 724
 
4.9%
Other values (12) 4047
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3368
22.8%
l 1077
 
7.3%
n 876
 
5.9%
a 859
 
5.8%
s 824
 
5.6%
760
 
5.1%
o 758
 
5.1%
m 758
 
5.1%
t 724
 
4.9%
D 724
 
4.9%
Other values (12) 4047
27.4%

EmpJobRole
Categorical

High correlation 

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
Sales Executive
270 
Developer
236 
Manager R&D
94 
Research Scientist
77 
Sales Representative
69 
Other values (14)
454 

Length

Max length25
Median length21.5
Mean length14.545
Min length7

Characters and Unicode

Total characters17454
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowSales Executive
3rd rowSales Executive
4th rowManager
5th rowSales Executive

Common Values

ValueCountFrequency (%)
Sales Executive 270
22.5%
Developer 236
19.7%
Manager R&D 94
 
7.8%
Research Scientist 77
 
6.4%
Sales Representative 69
 
5.8%
Laboratory Technician 64
 
5.3%
Senior Developer 52
 
4.3%
Manager 51
 
4.2%
Finance Manager 49
 
4.1%
Human Resources 45
 
3.8%
Other values (9) 193
16.1%

Length

2025-03-19T18:29:29.610770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sales 339
15.9%
developer 288
13.5%
executive 270
12.7%
manager 221
10.4%
r&d 109
 
5.1%
representative 102
 
4.8%
scientist 97
 
4.6%
research 96
 
4.5%
senior 67
 
3.1%
laboratory 64
 
3.0%
Other values (14) 475
22.3%

Most occurring characters

ValueCountFrequency (%)
e 3179
18.2%
a 1536
 
8.8%
r 1136
 
6.5%
i 975
 
5.6%
928
 
5.3%
c 907
 
5.2%
n 901
 
5.2%
t 900
 
5.2%
s 788
 
4.5%
l 733
 
4.2%
Other values (24) 5471
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3179
18.2%
a 1536
 
8.8%
r 1136
 
6.5%
i 975
 
5.6%
928
 
5.3%
c 907
 
5.2%
n 901
 
5.2%
t 900
 
5.2%
s 788
 
4.5%
l 733
 
4.2%
Other values (24) 5471
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3179
18.2%
a 1536
 
8.8%
r 1136
 
6.5%
i 975
 
5.6%
928
 
5.3%
c 907
 
5.2%
n 901
 
5.2%
t 900
 
5.2%
s 788
 
4.5%
l 733
 
4.2%
Other values (24) 5471
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3179
18.2%
a 1536
 
8.8%
r 1136
 
6.5%
i 975
 
5.6%
928
 
5.3%
c 907
 
5.2%
n 901
 
5.2%
t 900
 
5.2%
s 788
 
4.5%
l 733
 
4.2%
Other values (24) 5471
31.3%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.6 KiB
Travel_Rarely
846 
Travel_Frequently
222 
Non-Travel
132 

Length

Max length17
Median length13
Mean length13.41
Min length10

Characters and Unicode

Total characters16092
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Rarely
3rd rowTravel_Frequently
4th rowTravel_Rarely
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 846
70.5%
Travel_Frequently 222
 
18.5%
Non-Travel 132
 
11.0%

Length

2025-03-19T18:29:29.732562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:29.810398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 846
70.5%
travel_frequently 222
 
18.5%
non-travel 132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
e 2490
15.5%
r 2268
14.1%
l 2268
14.1%
a 2046
12.7%
T 1200
7.5%
v 1200
7.5%
y 1068
6.6%
_ 1068
6.6%
R 846
 
5.3%
n 354
 
2.2%
Other values (7) 1284
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2490
15.5%
r 2268
14.1%
l 2268
14.1%
a 2046
12.7%
T 1200
7.5%
v 1200
7.5%
y 1068
6.6%
_ 1068
6.6%
R 846
 
5.3%
n 354
 
2.2%
Other values (7) 1284
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2490
15.5%
r 2268
14.1%
l 2268
14.1%
a 2046
12.7%
T 1200
7.5%
v 1200
7.5%
y 1068
6.6%
_ 1068
6.6%
R 846
 
5.3%
n 354
 
2.2%
Other values (7) 1284
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2490
15.5%
r 2268
14.1%
l 2268
14.1%
a 2046
12.7%
T 1200
7.5%
v 1200
7.5%
y 1068
6.6%
_ 1068
6.6%
R 846
 
5.3%
n 354
 
2.2%
Other values (7) 1284
8.0%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1658333
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:29.907546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1766363
Coefficient of variation (CV)0.89207778
Kurtosis-0.24201678
Mean9.1658333
Median Absolute Deviation (MAD)5
Skewness0.96295612
Sum10999
Variance66.85738
MonotonicityNot monotonic
2025-03-19T18:29:30.030469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 184
15.3%
1 170
14.2%
8 69
 
5.8%
3 67
 
5.6%
10 66
 
5.5%
9 66
 
5.5%
7 65
 
5.4%
5 54
 
4.5%
4 51
 
4.2%
6 46
 
3.8%
Other values (19) 362
30.2%
ValueCountFrequency (%)
1 170
14.2%
2 184
15.3%
3 67
 
5.6%
4 51
 
4.2%
5 54
 
4.5%
6 46
 
3.8%
7 65
 
5.4%
8 69
 
5.8%
9 66
 
5.5%
10 66
 
5.5%
ValueCountFrequency (%)
29 23
1.9%
28 20
1.7%
27 9
 
0.8%
26 22
1.8%
25 19
1.6%
24 23
1.9%
23 22
1.8%
22 17
1.4%
21 15
1.2%
20 19
1.6%
Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
3
449 
4
322 
2
239 
1
148 
5
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Length

2025-03-19T18:29:30.160364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:30.250937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring characters

ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 449
37.4%
4 322
26.8%
2 239
19.9%
1 148
 
12.3%
5 42
 
3.5%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
3
367 
4
361 
2
242 
1
230 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row2
5th row1

Common Values

ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Length

2025-03-19T18:29:30.361418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:30.440488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring characters

ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 367
30.6%
4 361
30.1%
2 242
20.2%
1 230
19.2%

EmpHourlyRate
Real number (ℝ)

Distinct71
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.981667
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:30.609390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.211302
Coefficient of variation (CV)0.30631694
Kurtosis-1.1868905
Mean65.981667
Median Absolute Deviation (MAD)18
Skewness-0.035164888
Sum79178
Variance408.49674
MonotonicityNot monotonic
2025-03-19T18:29:30.766483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 25
 
2.1%
79 25
 
2.1%
42 24
 
2.0%
57 23
 
1.9%
46 23
 
1.9%
92 22
 
1.8%
72 22
 
1.8%
45 22
 
1.8%
96 22
 
1.8%
94 21
 
1.8%
Other values (61) 971
80.9%
ValueCountFrequency (%)
30 13
1.1%
31 13
1.1%
32 19
1.6%
33 16
1.3%
34 6
 
0.5%
35 14
1.2%
36 17
1.4%
37 13
1.1%
38 10
0.8%
39 14
1.2%
ValueCountFrequency (%)
100 14
1.2%
99 19
1.6%
98 20
1.7%
97 18
1.5%
96 22
1.8%
95 17
1.4%
94 21
1.8%
93 13
1.1%
92 22
1.8%
91 14
1.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
3
724 
2
294 
4
112 
1
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Length

2025-03-19T18:29:30.946409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:31.153627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring characters

ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 724
60.3%
2 294
24.5%
4 112
 
9.3%
1 70
 
5.8%

EmpJobLevel
Categorical

High correlation 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
2
441 
1
440 
3
173 
4
90 
5
56 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row5
5th row2

Common Values

ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Length

2025-03-19T18:29:31.301668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:31.384366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring characters

ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 441
36.8%
1 440
36.7%
3 173
 
14.4%
4 90
 
7.5%
5 56
 
4.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
4
378 
3
354 
2
237 
1
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row4
5th row1

Common Values

ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Length

2025-03-19T18:29:31.504502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:31.586853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring characters

ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 378
31.5%
3 354
29.5%
2 237
19.8%
1 231
19.2%

NumCompaniesWorked
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.665
Minimum0
Maximum9
Zeros156
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:31.694434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4693838
Coefficient of variation (CV)0.92659806
Kurtosis0.068862995
Mean2.665
Median Absolute Deviation (MAD)1
Skewness1.0486348
Sum3198
Variance6.0978565
MonotonicityNot monotonic
2025-03-19T18:29:31.782619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 433
36.1%
0 156
 
13.0%
3 133
 
11.1%
2 123
 
10.2%
4 107
 
8.9%
7 60
 
5.0%
6 56
 
4.7%
5 53
 
4.4%
8 40
 
3.3%
9 39
 
3.2%
ValueCountFrequency (%)
0 156
 
13.0%
1 433
36.1%
2 123
 
10.2%
3 133
 
11.1%
4 107
 
8.9%
5 53
 
4.4%
6 56
 
4.7%
7 60
 
5.0%
8 40
 
3.3%
9 39
 
3.2%
ValueCountFrequency (%)
9 39
 
3.2%
8 40
 
3.3%
7 60
 
5.0%
6 56
 
4.7%
5 53
 
4.4%
4 107
 
8.9%
3 133
 
11.1%
2 123
 
10.2%
1 433
36.1%
0 156
 
13.0%

OverTime
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
847 
True
353 
ValueCountFrequency (%)
False 847
70.6%
True 353
29.4%
2025-03-19T18:29:31.863382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

EmpLastSalaryHikePercent
Real number (ℝ)

Distinct15
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.2225
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:32.019538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6259182
Coefficient of variation (CV)0.23819466
Kurtosis-0.29974078
Mean15.2225
Median Absolute Deviation (MAD)2
Skewness0.80865363
Sum18267
Variance13.147283
MonotonicityNot monotonic
2025-03-19T18:29:32.329644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
14 172
14.3%
11 169
14.1%
13 168
14.0%
12 155
12.9%
15 82
6.8%
18 73
6.1%
16 68
 
5.7%
17 67
 
5.6%
19 63
 
5.2%
20 50
 
4.2%
Other values (5) 133
11.1%
ValueCountFrequency (%)
11 169
14.1%
12 155
12.9%
13 168
14.0%
14 172
14.3%
15 82
6.8%
16 68
 
5.7%
17 67
 
5.6%
18 73
6.1%
19 63
 
5.2%
20 50
 
4.2%
ValueCountFrequency (%)
25 13
 
1.1%
24 18
 
1.5%
23 21
 
1.8%
22 47
3.9%
21 34
2.8%
20 50
4.2%
19 63
5.2%
18 73
6.1%
17 67
5.6%
16 68
5.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
3
379 
4
355 
2
247 
1
219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Length

2025-03-19T18:29:32.616553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:32.786502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring characters

ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 379
31.6%
4 355
29.6%
2 247
20.6%
1 219
18.2%

TotalWorkExperienceInYears
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.33
Minimum0
Maximum40
Zeros10
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:33.044241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.797228
Coefficient of variation (CV)0.68819311
Kurtosis0.80563333
Mean11.33
Median Absolute Deviation (MAD)4
Skewness1.0868619
Sum13596
Variance60.796764
MonotonicityNot monotonic
2025-03-19T18:29:33.379812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 159
 
13.2%
6 105
 
8.8%
8 85
 
7.1%
9 77
 
6.4%
5 71
 
5.9%
1 65
 
5.4%
7 61
 
5.1%
4 51
 
4.2%
12 37
 
3.1%
15 34
 
2.8%
Other values (30) 455
37.9%
ValueCountFrequency (%)
0 10
 
0.8%
1 65
5.4%
2 26
 
2.2%
3 34
 
2.8%
4 51
4.2%
5 71
5.9%
6 105
8.8%
7 61
5.1%
8 85
7.1%
9 77
6.4%
ValueCountFrequency (%)
40 1
 
0.1%
38 1
 
0.1%
37 3
 
0.2%
36 4
0.3%
35 2
 
0.2%
34 5
0.4%
33 7
0.6%
32 8
0.7%
31 7
0.6%
30 5
0.4%

TrainingTimesLastYear
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7858333
Minimum0
Maximum6
Zeros44
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:33.619769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2634462
Coefficient of variation (CV)0.4535254
Kurtosis0.56753103
Mean2.7858333
Median Absolute Deviation (MAD)1
Skewness0.5320732
Sum3343
Variance1.5962962
MonotonicityNot monotonic
2025-03-19T18:29:33.816628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 445
37.1%
3 413
34.4%
4 98
 
8.2%
5 98
 
8.2%
1 56
 
4.7%
6 46
 
3.8%
0 44
 
3.7%
ValueCountFrequency (%)
0 44
 
3.7%
1 56
 
4.7%
2 445
37.1%
3 413
34.4%
4 98
 
8.2%
5 98
 
8.2%
6 46
 
3.8%
ValueCountFrequency (%)
6 46
 
3.8%
5 98
 
8.2%
4 98
 
8.2%
3 413
34.4%
2 445
37.1%
1 56
 
4.7%
0 44
 
3.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
3
727 
2
294 
4
115 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Length

2025-03-19T18:29:34.048780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:34.225934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring characters

ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 727
60.6%
2 294
24.5%
4 115
 
9.6%
1 64
 
5.3%

ExperienceYearsAtThisCompany
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0775
Minimum0
Maximum40
Zeros36
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:34.475332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q310
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.236899
Coefficient of variation (CV)0.88122911
Kurtosis4.0579594
Mean7.0775
Median Absolute Deviation (MAD)3
Skewness1.789055
Sum8493
Variance38.89891
MonotonicityNot monotonic
2025-03-19T18:29:34.818414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 152
12.7%
1 138
11.5%
2 107
8.9%
3 105
8.8%
10 100
8.3%
4 88
 
7.3%
7 73
 
6.1%
6 66
 
5.5%
9 66
 
5.5%
8 63
 
5.2%
Other values (27) 242
20.2%
ValueCountFrequency (%)
0 36
 
3.0%
1 138
11.5%
2 107
8.9%
3 105
8.8%
4 88
7.3%
5 152
12.7%
6 66
5.5%
7 73
6.1%
8 63
5.2%
9 66
5.5%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.2%
34 1
 
0.1%
33 5
0.4%
32 3
0.2%
31 2
 
0.2%
30 1
 
0.1%
29 2
 
0.2%
27 2
 
0.2%

ExperienceYearsInCurrentRole
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2916667
Minimum0
Maximum18
Zeros190
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:35.133718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6137441
Coefficient of variation (CV)0.84203746
Kurtosis0.43802869
Mean4.2916667
Median Absolute Deviation (MAD)3
Skewness0.88815867
Sum5150
Variance13.059147
MonotonicityNot monotonic
2025-03-19T18:29:35.310355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 303
25.2%
0 190
15.8%
7 176
14.7%
3 107
 
8.9%
4 92
 
7.7%
8 78
 
6.5%
9 63
 
5.2%
1 46
 
3.8%
6 30
 
2.5%
5 29
 
2.4%
Other values (9) 86
 
7.2%
ValueCountFrequency (%)
0 190
15.8%
1 46
 
3.8%
2 303
25.2%
3 107
 
8.9%
4 92
 
7.7%
5 29
 
2.4%
6 30
 
2.5%
7 176
14.7%
8 78
 
6.5%
9 63
 
5.2%
ValueCountFrequency (%)
18 2
 
0.2%
17 3
 
0.2%
16 7
 
0.6%
15 4
 
0.3%
14 10
 
0.8%
13 10
 
0.8%
12 7
 
0.6%
11 18
 
1.5%
10 25
 
2.1%
9 63
5.2%

YearsSinceLastPromotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1941667
Minimum0
Maximum15
Zeros469
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:35.471895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2215599
Coefficient of variation (CV)1.4682385
Kurtosis3.5390801
Mean2.1941667
Median Absolute Deviation (MAD)1
Skewness1.9749316
Sum2633
Variance10.378448
MonotonicityNot monotonic
2025-03-19T18:29:35.577367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 469
39.1%
1 297
24.8%
2 127
 
10.6%
7 62
 
5.2%
4 53
 
4.4%
3 45
 
3.8%
5 35
 
2.9%
6 24
 
2.0%
11 23
 
1.9%
9 16
 
1.3%
Other values (6) 49
 
4.1%
ValueCountFrequency (%)
0 469
39.1%
1 297
24.8%
2 127
 
10.6%
3 45
 
3.8%
4 53
 
4.4%
5 35
 
2.9%
6 24
 
2.0%
7 62
 
5.2%
8 11
 
0.9%
9 16
 
1.3%
ValueCountFrequency (%)
15 11
 
0.9%
14 5
 
0.4%
13 8
 
0.7%
12 9
 
0.8%
11 23
 
1.9%
10 5
 
0.4%
9 16
 
1.3%
8 11
 
0.9%
7 62
5.2%
6 24
 
2.0%

YearsWithCurrManager
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.105
Minimum0
Maximum17
Zeros215
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-19T18:29:35.706527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.541576
Coefficient of variation (CV)0.8627469
Kurtosis0.14820164
Mean4.105
Median Absolute Deviation (MAD)3
Skewness0.8131583
Sum4926
Variance12.542761
MonotonicityNot monotonic
2025-03-19T18:29:35.832451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 281
23.4%
0 215
17.9%
7 176
14.7%
3 103
 
8.6%
8 87
 
7.2%
4 85
 
7.1%
1 67
 
5.6%
9 53
 
4.4%
6 28
 
2.3%
5 26
 
2.2%
Other values (8) 79
 
6.6%
ValueCountFrequency (%)
0 215
17.9%
1 67
 
5.6%
2 281
23.4%
3 103
 
8.6%
4 85
 
7.1%
5 26
 
2.2%
6 28
 
2.3%
7 176
14.7%
8 87
 
7.2%
9 53
 
4.4%
ValueCountFrequency (%)
17 6
 
0.5%
16 2
 
0.2%
15 3
 
0.2%
14 2
 
0.2%
13 10
 
0.8%
12 14
 
1.2%
11 20
 
1.7%
10 22
 
1.8%
9 53
4.4%
8 87
7.2%

Attrition
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
1022 
True
178 
ValueCountFrequency (%)
False 1022
85.2%
True 178
 
14.8%
2025-03-19T18:29:35.946429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
3
874 
2
194 
4
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Length

2025-03-19T18:29:36.054478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T18:29:36.176908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring characters

ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 874
72.8%
2 194
 
16.2%
4 132
 
11.0%

Interactions

2025-03-19T18:29:23.737551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:01.251325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.793296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:05.085094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.491304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.909500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:11.910549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.721974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:15.712566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:18.575470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:20.430429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:23.981406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:01.425504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.897433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:05.315330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.612409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:09.011060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:12.180414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.931184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:15.963544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:18.757152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:20.748801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.105834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:01.566611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:03.051800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:05.711852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.721416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:09.231355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:12.457342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.046747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:16.220357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:18.898825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:21.042716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.224631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:01.675282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:03.161307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:05.955590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.860758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:09.465680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:12.632597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.202249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:16.558539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:19.024062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:21.319847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.364418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:01.775347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:03.270285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:06.189645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.958432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:09.685435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:12.742662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.304321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:16.807334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:19.191425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:21.535252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.488256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:01.909549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:03.436971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:06.435046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.078500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:09.989999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:12.877887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.415398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:17.095060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:19.340439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:21.880672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.625666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.023402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:03.681208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:06.735542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.206710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:10.432761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.001796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.532658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:17.421108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:19.465585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:22.288413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.744892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.128702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:04.012104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:06.871257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.420358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:10.678677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.123586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.638784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:17.760896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:19.773527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:22.517671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:24.935591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.296651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:04.274512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.038721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.547644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:11.044245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.329191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:14.866632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:18.044625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:19.919541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:22.827036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:25.112984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.405358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:04.529532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.214622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.690669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:11.370675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.466784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:15.122476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:18.263529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:20.085434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:23.172840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:25.276391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:02.680501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:04.815874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:07.336597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:08.800566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:11.605439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:13.584475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:15.390374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:18.406655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:20.247454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-19T18:29:23.404574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-19T18:29:36.450526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAttritionBusinessTravelFrequencyDistanceFromHomeEducationBackgroundEmpDepartmentEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobRoleEmpJobSatisfactionEmpLastSalaryHikePercentEmpRelationshipSatisfactionEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleGenderMaritalStatusNumCompaniesWorkedOverTimePerformanceRatingTotalWorkExperienceInYearsTrainingTimesLastYearYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2580.047-0.0030.0000.0000.1530.0620.0610.0000.2950.1290.0000.0010.0000.0460.2540.2030.0000.1330.3420.0000.0000.6520.0010.1870.196
Attrition0.2581.0000.1240.0510.1020.0590.0000.1310.0380.1500.2170.1630.0810.0000.0000.0790.1840.1800.0170.1820.1100.2200.0220.2300.0320.0450.179
BusinessTravelFrequency0.0470.1241.0000.0500.0000.0000.0000.0000.0000.0400.0170.0000.0000.0320.0000.0000.0000.0000.0380.0490.0000.0490.0130.0000.0000.0410.084
DistanceFromHome-0.0030.0510.0501.0000.0000.0000.0000.032-0.0060.0060.0600.0000.0000.0320.0250.0000.0280.0230.0200.000-0.0090.0800.0480.017-0.0120.0090.017
EducationBackground0.0000.1020.0000.0001.0000.3610.0630.0520.0120.0000.0880.3330.0500.0000.0460.0370.0000.0000.0000.0000.0590.0000.0090.0320.0480.0000.000
EmpDepartment0.0000.0590.0000.0000.3611.0000.0000.0000.0000.0370.1420.9750.0330.0000.0530.0470.0000.0000.0000.0390.0230.0260.1590.0000.0330.0000.000
EmpEducationLevel0.1530.0000.0000.0000.0630.0001.0000.0000.0360.0000.0870.0650.0220.0160.0000.0000.0760.0400.0480.0000.1000.0000.0240.1010.0420.0000.034
EmpEnvironmentSatisfaction0.0620.1310.0000.0320.0520.0000.0001.0000.0000.0400.0230.0000.0000.0000.0000.0000.0180.0500.0000.0360.0000.0580.3650.0000.0000.0000.000
EmpHourlyRate0.0610.0380.000-0.0060.0120.0000.0360.0001.0000.0330.0000.0000.000-0.0110.0000.000-0.013-0.0230.0000.0000.0340.0640.0620.010-0.013-0.034-0.002
EmpJobInvolvement0.0000.1500.0400.0060.0000.0370.0000.0400.0331.0000.0000.0360.0000.0230.0000.0000.0620.0030.0000.0240.0000.0000.0000.0000.0000.0000.045
EmpJobLevel0.2950.2170.0170.0600.0880.1420.0870.0230.0000.0001.0000.4140.0000.0000.0000.0000.3500.2420.0480.0540.1100.0000.0380.5390.0000.2110.227
EmpJobRole0.1290.1630.0000.0000.3330.9750.0650.0000.0000.0360.4141.0000.0530.0070.0560.0650.1290.0900.1040.0000.0720.0700.1600.2180.0110.0310.075
EmpJobSatisfaction0.0000.0810.0000.0000.0500.0330.0220.0000.0000.0000.0000.0531.0000.0000.0000.0000.0000.0260.0000.0000.0000.0470.0490.0000.0250.0000.000
EmpLastSalaryHikePercent0.0010.0000.0320.0320.0000.0000.0160.000-0.0110.0230.0000.0070.0001.0000.0330.000-0.039-0.0230.0280.000-0.0030.0000.478-0.016-0.015-0.055-0.018
EmpRelationshipSatisfaction0.0000.0000.0000.0250.0460.0530.0000.0000.0000.0000.0000.0560.0000.0331.0000.0000.0000.0000.0000.0430.0000.0000.0000.0000.0000.0580.000
EmpWorkLifeBalance0.0460.0790.0000.0000.0370.0470.0000.0000.0000.0000.0000.0650.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0970.0340.0000.0000.038
ExperienceYearsAtThisCompany0.2540.1840.0000.0280.0000.0000.0760.018-0.0130.0620.3500.1290.000-0.0390.0000.0001.0000.8700.0470.038-0.1730.0000.1030.5930.0020.5200.840
ExperienceYearsInCurrentRole0.2030.1800.0000.0230.0000.0000.0400.050-0.0230.0030.2420.0900.026-0.0230.0000.0000.8701.0000.0660.052-0.1320.0610.1910.4990.0140.5130.746
Gender0.0000.0170.0380.0200.0000.0000.0480.0000.0000.0000.0480.1040.0000.0280.0000.0000.0470.0661.0000.0110.0000.0220.0000.0270.0000.0000.000
MaritalStatus0.1330.1820.0490.0000.0000.0390.0000.0360.0000.0240.0540.0000.0000.0000.0430.0000.0380.0520.0111.0000.0480.0000.0300.0600.0080.0110.018
NumCompaniesWorked0.3420.1100.000-0.0090.0590.0230.1000.0000.0340.0000.1100.0720.000-0.0030.0000.000-0.173-0.1320.0000.0481.0000.0000.0000.313-0.040-0.064-0.142
OverTime0.0000.2200.0490.0800.0000.0260.0000.0580.0640.0000.0000.0700.0470.0000.0000.0000.0000.0610.0220.0000.0001.0000.0880.0000.0990.0000.000
PerformanceRating0.0000.0220.0130.0480.0090.1590.0240.3650.0620.0000.0380.1600.0490.4780.0000.0970.1030.1910.0000.0300.0000.0881.0000.0630.0000.1820.131
TotalWorkExperienceInYears0.6520.2300.0000.0170.0320.0000.1010.0000.0100.0000.5390.2180.000-0.0160.0000.0340.5930.4990.0270.0600.3130.0000.0631.000-0.0130.3420.490
TrainingTimesLastYear0.0010.0320.000-0.0120.0480.0330.0420.000-0.0130.0000.0000.0110.025-0.0150.0000.0000.0020.0140.0000.008-0.0400.0990.000-0.0131.0000.034-0.021
YearsSinceLastPromotion0.1870.0450.0410.0090.0000.0000.0000.000-0.0340.0000.2110.0310.000-0.0550.0580.0000.5200.5130.0000.011-0.0640.0000.1820.3420.0341.0000.456
YearsWithCurrManager0.1960.1790.0840.0170.0000.0000.0340.000-0.0020.0450.2270.0750.000-0.0180.0000.0380.8400.7460.0000.018-0.1420.0000.1310.490-0.0210.4561.000

Missing values

2025-03-19T18:29:25.640398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-19T18:29:25.972032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyDistanceFromHomeEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobSatisfactionNumCompaniesWorkedOverTimeEmpLastSalaryHikePercentEmpRelationshipSatisfactionTotalWorkExperienceInYearsTrainingTimesLastYearEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionPerformanceRating
032MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1034553241No124102210708No3
147MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1444423212No12420237717No3
240MaleLife SciencesMarriedSalesSales ExecutiveTravel_Frequently544482315Yes21320231813112No4
341MaleHuman ResourcesDivorcedHuman ResourcesManagerTravel_Rarely1042732543No1522322216126No3
460MaleMarketingSingleSalesSales ExecutiveTravel_Rarely1641843218No14410132222No3
527MaleLife SciencesDivorcedDevelopmentDeveloperTravel_Frequently1024323311No2139429717No4
650MaleMarketingMarriedSalesSales RepresentativeTravel_Rarely844543127No1544232222No3
728FemaleLife SciencesSingleDevelopmentDeveloperTravel_Rarely121671127Yes13410437737Yes3
836FemaleLife SciencesMarriedDevelopmentDeveloperNon-Travel831634319No14110238705No3
938FemaleLife SciencesSingleDevelopmentDeveloperTravel_Rarely133813334Yes14410441000No3
AgeGenderEducationBackgroundMaritalStatusEmpDepartmentEmpJobRoleBusinessTravelFrequencyDistanceFromHomeEmpEducationLevelEmpEnvironmentSatisfactionEmpHourlyRateEmpJobInvolvementEmpJobLevelEmpJobSatisfactionNumCompaniesWorkedOverTimeEmpLastSalaryHikePercentEmpRelationshipSatisfactionTotalWorkExperienceInYearsTrainingTimesLastYearEmpWorkLifeBalanceExperienceYearsAtThisCompanyExperienceYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAttritionPerformanceRating
119023MaleMedicalMarriedDevelopmentDeveloperTravel_Rarely431584112No1615343202No4
119125MaleLife SciencesMarriedSalesSales ExecutiveTravel_Rarely834574220No2234433212No4
119238FemaleMarketingSingleSalesSales ExecutiveTravel_Rarely744462240No2018237705No4
119329MaleLife SciencesDivorcedDevelopmentDeveloperTravel_Frequently142761141No184105310728No3
119448MaleMarketingMarriedSalesSales ExecutiveTravel_Rarely212564223No12412332222No3
119527FemaleMedicalDivorcedSalesSales ExecutiveTravel_Frequently314714241Yes2026336504No4
119637MaleLife SciencesSingleDevelopmentSenior DeveloperTravel_Rarely1024804143No1714231000No3
119750MaleMedicalMarriedDevelopmentSenior DeveloperTravel_Rarely2814744131Yes113203320838No3
119834FemaleMedicalSingleData ScienceData ScientistTravel_Rarely934462321No1429348777No3
119924FemaleLife SciencesSingleSalesSales ExecutiveTravel_Rarely321653239No1414332220Yes2